Stereo depth estimation using deep neural networks

ABSTRACT

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/646,148, filed on Mar. 21, 2018, which is hereby incorporated byreference in its entirety.

BACKGROUND

Depth estimation plays an important role in computer vision techniques,especially in technology areas such as autonomous driving, advanceddriver assistance systems (ADAS), unmanned aerial vehicle (UAV) control,robotics, virtual reality, and augmented reality, to name a few. Forexample, depth estimation may be useful for understanding a real-worldenvironment for navigation, obstacle avoidance, localization, mapping,reconstruction of the real-world environment in a virtual world, and/orother uses.

Conventional approaches to depth estimation have deployed deep neuralnetworks (DNNs) that use either monocular or stereoscopic images asinput to compute depth information as an output. With respect tomonocular based DNNs, monocular based DNNs suffer from inaccuracies indepth information because there is only a single frame of reference. Asa result, a DNN using monocular images may be trained to only detectobjects and associated behaviors that were included in the training data(e.g., the monocular images). For example, the DNN may be unable toaccurately detect depth for a pedestrian running, or jumping, or may beunable to accurately predict depth for a larger vehicle, or a smallervehicle, or another behavior, vehicle, or object that is not within thetypes of behaviors, vehicles, or objects included within the trainingdata. This may be a result of the DNN learning to predict distancesbased on relative sizes of known objects performing known behaviorswithin the training data (e.g., a larger vehicle at a further distancemay appear smaller in the monocular image, and thus may be predicted tobe further away by the DNN). These inaccurate results may lead to a lackof efficacy of the DNN in actual deployment, especially where safety isan important factor and depth information is used by the system toperform safely (e.g., for autonomous vehicles, UAVs, robots, etc.)

In addition, DNNs trained on monocular image data may need to beseparately trained for the geographic regions in which they will bedeployed because the DNN's may be trained to identify specific vehiclesand behaviors in specific environments. As a result, vehicles,behaviors, and environments in one region may not be similar enough toanother region, thereby requiring separate training for each region. Forexample, a DNN that is to be used in Germany may need to be trainedusing data from Germany, and a DNN that is to be used in Portland, mayneed to be trained using data from Portland. As a result, training ofmonocular DNNs may not be universal, and thus may require separatetraining in different localities—thereby increasing the computingrequirements necessary for deploying the DNN in multiple locations.

As indicated herein, other conventional approaches have usedstereoscopic images captured concurrently as input to a DNN to generatedepth information. These conventional approaches use either only LIDARdata or only photometric consistency to train the DNN. Where only sparseLIDAR data is used to train the DNN, the outputs (e.g., disparity maps)of the DNN may be inaccurate (e.g., missing detections of some objectscompletely) and noisy. This may be a result of the sparse nature ofLIDAR data. In addition, because LIDAR data may be representative ofunwanted artifacts—such as lines—the training data may need to beaugmented to train the DNN not to identify these artifacts in theoutput, which may increase the noise of the output. In addition, theoutput may need to undergo additional filtering and post-processing inorder to smooth the results, thereby increasing the processingrequirements and slowing the run-time of the system deploying the DNN.

With respect to the architecture of conventional stereoscopic DNNs,rectified linear unit (ReLU) activation functions may be used, which maynecessitate batch normalization layers (e.g., on outputs ofconvolutional layers prior to being input to the ReLU) therebyincreasing the overall size of the DNN. In addition, within matchinglayers of these conventional DNNs, only fully three-dimensional (3D)convolutions may be used which may further increase the overall size ofthe DNN. By increasing the size of the DNN (e.g., the number of layersand/or nodes), the processing requirements and the run-time may beincreased, thereby decreasing the likelihood of accurate and effectivedeployment of the DNN in real-time.

In addition, the conventional DNNs—in order to generate final disparityvalues for each of the input images—may use a softmax function(alternatively referred to as a softargmax function) on an output ofmatching layers of the DNN (e.g., layers of the DNN that performmatching between pixels of left and right images). However, a softmaxfunction may have the drawback of assuming that all context has alreadybeen taken into account, which will not always be the case. As such,where repeatable textures appear, the outputs of the softmax functionmay include false positives and maximums where there should not bemaximums, thereby reducing the accuracy of the DNN. As a result, thedepth values determined from the final disparity values may beinaccurate, which may reduce the accuracy and thus the safety of thesystem deploying the DNN.

SUMMARY

Embodiments of the present disclosure relate to stereo depth estimationusing deep neural networks. Systems and methods are disclosed that mayuse semi-supervised training to train a deep neural network to predictdepth from stereoscopic images.

In contrast to conventional systems, such as those described above, thepresent disclosure includes stereoscopic deep neural networks (DNN) thatmay produce comparatively more accurate and reliable results while beingdeployable in real-time. For example, both LIDAR data (supervisedtraining) and photometric error (unsupervised training) may be used totrain the DNN in a semi-supervised manner By using both photometricerror and LIDAR data, the benefits of each may be learned by the DNNwhile the drawbacks to each may be mitigated by the other.

Other benefits of the stereoscopic DNNs of the present disclosure mayrelate to the network architecture. For example, instead of ReLUactivation functions used in conventional DNNs, an exponential linearunit (ELU) activation function may be used by a DNN of the presentdisclosure. By using the ELU activation function, the DNN may requirecomparatively less layers (e.g., may not require batch normalizationlayers), thus decreasing the overall size of the DNN and resulting infaster run-times while increasing accuracy. In addition, instead ofusing a soft argmax function, a machine learned (ML) argmaxfunction—including a plurality of convolutional layers with trainableparameters (e.g., via backpropagation)—may be used by the DNN to accountfor context. Further, layers of the DNN may include an encoder/decoder“bottleneck” architecture, where the encoder portion of the matchinglayers includes a combination of three-dimensional (3D) convolutionallayers followed by two-dimensional (2D) convolutional layers—as opposedto conventional systems that use only 3D convolutional layers. Byreducing the number of 3D convolutional layers and replacing them with2D convolutional layers, less processing may be required withoutsacrificing accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for stereo depth estimation using deepneural networks are described in detail below with reference to theattached drawing figures, wherein:

FIG. 1A is an illustration of a stereoscopic deep neural network fordepth estimation using stereoscopic images, in accordance with someembodiments of the present disclosure;

FIG. 1B is a table of example architectures for machine learning models,in accordance with some embodiments of the present disclosure;

FIG. 1C is a table comparing accuracy of different architectures formachine learning models, in accordance with some embodiments of thepresent disclosure;

FIG. 1D is a table comparing run-time of different architectures formachine learning models on various graphics processing units, inaccordance with some embodiments of the present disclosure;

FIG. 2 is a flow diagram illustrating an example method for predictingdepth from stereoscopic images using a machine learning model, inaccordance with some embodiments of the present disclosure;

FIG. 3A is a data flow diagram illustrating a process for training amachine learning model for depth estimation using stereoscopic images,in accordance with some embodiments of the present disclosure;

FIG. 3B is a table comparing accuracy of different training methods formachine learning models, in accordance with some embodiments of thepresent disclosure;

FIG. 3C includes an illustration of example disparity maps based onoutputs machine learning models, in accordance with some embodiments ofthe present disclosure;

FIG. 4 is a flow diagram illustrating an example method for training amachine learning model to predict depth from stereoscopic images, inaccordance with some embodiments of the present disclosure;

FIG. 5A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 5B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 5A, in accordance with someembodiments of the present disclosure;

FIG. 5C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 5A, in accordance with someembodiments of the present disclosure;

FIG. 5D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 5A, in accordancewith some embodiments of the present disclosure; and

FIG. 6 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to stereo depth estimationusing deep neural networks. The present disclosure may be described withrespect to an example autonomous vehicle 500 (alternatively referred toherein as “vehicle 500” or “autonomous vehicle 500”), an example ofwhich is described in more detail herein with respect to FIGS. 5A-5D.However, this is not intended to be limiting. For example, and withoutdeparting from the scope of the present disclosure, the systems,methods, and/or processes described herein may be applicable tonon-autonomous vehicles, robots, unmanned aerial vehicles, virtualreality (VR) systems, augmented reality (AR) systems, and/or any othertype of technology that may use depth information.

Stereoscopic Deep Neural Network Architecture

In contrast to conventional systems, such as those described above, thepresent disclosure provides for stereoscopic deep neural networks (DNN)that may produce comparatively more accurate and reliable results whilebeing deployable in real-time. Benefits of the stereoscopic DNNs of thepresent disclosure may relate to the network architecture. For example,a DNN may not include any rectified linear unit (ReLU) activationfunctions, thereby removing the need for batch normalization layers andreducing the overall size and computing requirements for deployment ofthe DNN as compared to conventional stereo DNNs. Instead of ReLUactivation functions, an exponential linear unit (ELU) activationfunction may be used by a DNN of the present disclosure. By using theELU activation function, the DNN may require comparatively less layers,thus decreasing the overall size of the DNN, resulting in fasterrun-times, and increasing accuracy.

In addition, instead of using a soft argmax function (alternativelyreferred to as a softmax function), a machine learned (ML) argmaxfunction may be used by a DNN of the present disclosure. The ML argmaxfunction may include a plurality of convolutional layers and may includetrainable parameters (e.g., trainable using backpropagation) in order toaccount for context, unlike softmax activation functions. In addition,in some examples, the ML argmax function may include an encoder/decoder“bottleneck” (e.g., convolutional layers followed by deconvolutionallayers) architecture, that may further increase the processing speeds ofthe DNN.

Further, matching layers of a DNN may further include an encoder/decoder“bottleneck” architecture, where the encoder portion of the matchinglayers includes a combination of three-dimensional (3D) convolutionallayers followed by two-dimensional (2D) convolutional layers, as opposedto conventional systems that use only 3D convolutional layers. Byreducing the number of 3D convolutional layers and replacing them with2D convolutional layers, less processing may be required withoutsacrificing accuracy. For example, because one or more 3D convolutionallayers may be still be used, the benefits of the 3D convolutional layersmay still be realized, and using the 2D convolutional layers may serveto reduce the size of the DNN and the compute requirements for the DNN.

The outputs of a DNN in accordance with the present disclosure mayinclude disparity maps corresponding to each of the input images, andthe disparity maps may be used to calculate depth in the field of viewof the sensors (e.g., the stereo cameras). The depth information may beuseful for a robot, an autonomous vehicle, a drone, a virtual realitysystem, an augmented reality system, and/or another object or systemwhen navigating through space.

Now referring to FIG. 1A, FIG. 1A is an illustration of a stereoscopicdeep neural network 100 for depth estimation using stereoscopic images,in accordance with some embodiments of the present disclosure. Thestereoscopic deep neural network 100 may alternatively be referred toherein as “DNN 100.” The DNN 100 may generate disparity maps (e.g.,disparity map 116A and disparity map 116B) based on input data—such assensor data (e.g., stereoscopic image data representative of an image102A and an image 102B). The input data may include data representativeof the images 102 generated from a stereoscopic camera (e.g., one ormore stereo cameras 568) of the vehicle 500, or another object (e.g., arobot, a drone, a VR system, an AR system, etc.). In some examples, theimage 102A may include a left image (e.g., from a left image sensor of astereoscopic camera) and the image 102B may include a right image (e.g.,from a right image sensor of a stereoscopic camera). The images 102 mayhave a size of H×W×C, where H is a spatial height of the image, W is aspatial width of the image, and C is the number of input channels (e.g.,without limitation, C may be equal to 3). In some examples, each of theimages may be resized prior to input to the DNN 100. For a non-limitingexample, the images may be resized to 1024×320. The images 102 may be ofany color space, including but not limited to, RGB, HSL, HSV, YUV, YIQ,YDbDr, CIE, etc.

The stereoscopic camera may be calibrated such that disparity valuesbetween left and right images of the camera have known depth valueconversions. In some non-limiting examples, equation (1), below, may beused to determine depth values from disparity values:

Z=fB/d  (1)

where Z is the depth for a given pixel, f is focal length of a stereocamera, B is a baseline distance between a center of a first lens and acenter of a second lens of the stereo camera, and d is disparity.

As such, disparity values associated with pixels from the disparity maps116 may be converted to depth values (e.g., using equation (1), above).The depth values may then be used by any of a number of components ofthe vehicle 500, or another object or system. The components of thevehicle may include components from any layers of an autonomous drivingsoftware stack (e.g., world model management layers, perception layers,planning layers, control layers, obstacle avoidance layers, actuationlayers, etc.). For example, the depth values may be used to help thevehicle 500 navigate a physical environment based on an understanding ofa distance of objects from the vehicle 500 in the environment.

The DNN 100 may include a first stream or tower 118A (e.g., a left imagestream) corresponding to the first image 102A and a second stream ortower 118B (e.g., a right image stream) corresponding to the secondimage 102B. For example, the first stream 118A and the second stream118B may be executed in parallel to generate or compute the disparitymaps 116 from the images 102. In some examples, as indicated in FIG. 1A,layers or sets of layers from the first stream 118A may share weights orother parameters with corresponding layers or sets of layers from thesecond stream 118B. The corresponding layers or sets of layers may sharethe weights vertically (e.g., between layers of the first stream 118Aand layers of the second stream 118B). In addition to vertical sharingof weights, in some examples, the weights may be shared horizontally(e.g., between layers of the first stream 118A and other layers of thefirst stream 118A, and between layers of the second stream 118B andother layers of the second stream 118B).

Table 120 of FIG. 1B may include different architectures for the DNN100, and more specifically may include different architectures fordifferent features of the DNN 100. For example, the architecture offeature extraction layers 104A and 104B may be indicated by featureextraction column 124 of FIG. 1B, the architecture of cost volume layers106A and 106B may be indicated by cost volume column 126 of FIG. 1B, thearchitecture of matching layers 108A and 108B may be indicated bymatching column 128, the architecture of up-sampling layers 110A and110B may be indicated by up-sampling column 130, the architecture ofaggregator layers 112A and 112B may be indicated by aggregator column132, and so on. The different architectures of FIG. 1B may be forexample purposes only, and are not intended to be limiting. For example,architectures of one row in a column of the table 120 may be substitutedwith another row in the same column without departing from the scope ofthe present disclosure. In addition, different architectures may becontemplated other than those included in the table 120 withoutdeparting from the scope of the present disclosure. For example,additional or alternative layers or sets of layers (e.g., another oralternative column for the table 120) may be used depending on theembodiment.

The notation of the table 120 is mBk, where m is the number of blocks, Bis the type of block, and k is a number of layers in the block. Forexample, 1↓₁ means a single down-sampling layer, 1↑₁ means a singleup-sampling layer, 2C means two convolutional layers, and so on. Thesubscript ₊ indicates a residual connection, so 8(2C₊) means eightsuperblocks, where each superblock includes two blocks of singleconvolutional layers that accept residual connections.

Although the description herein may refer primarily to the ML argmaxmodel of the first row of the table 120, the other models in modelcolumn 122 may also be used. For example, each of the models—other thanthe ML argmax model—may use a soft argmax (or softmax) activationfunction as the aggregator at the aggregator layers 112 of the DNN 100.In some examples, rather than using concatenation for constructing orcomputing the cost volumes, one or more of the models may usecross-correlation (e.g., sliding dot product). In addition, one or moreof the models (e.g., the no bottleneck model) may use flat convolutionallayers rather than a bottleneck (e.g., encoder/decoder framework) forthe matching layers 108, or may user smaller bottleneck layers (e.g., inthe small/tiny model). Further, such as in the single tower model, oneof the two streams or towers 118 may be removed, and both of the images102 may be input into a single stream. In other examples, a smallernumber of weights or filters may be used than in the ML argmax model,such as in the small/tiny model. For example, the small model may useless filters and/or weights than the ML argmax model (e.g., in thematching layers 108), and the tiny model may uses even less filters(e.g., half as many 3D filters as in the small model) and/or weights.

The DNN 100 may include one or more feature extraction layers 104A and104B. The feature extraction layers 104A may compute a first feature mapcorresponding to the first image 102A and the feature extraction layers104B may compute a second feature map corresponding to the second image102B. The feature maps output by the feature extraction layers 104A and104B may be used by the cost volume layers 106A and 106B to compute thecost volumes, as described in more detail herein.

In some examples, a first layer of the feature extraction layers 104Aand 104B may include a down-sampling layer. The down-sampling layer maybe used reduce the size of the input to the feature extraction layers104A and 104B. In some non-limiting examples, the size may be reduced bya factor of two in each direction (e.g., height and width).Down-sampling may be executed to reduce both the computation and memoryuse in the cost volume layers 106A and 106B (e.g., because the output ofthe feature extraction layers 104A and 104B may be used to compute orgenerate the cost volumes). The feature extraction layers 104A and 104Bmay further include—after the down-sampling layer(s)—a number ofsuperblocks (e.g., 8), each including two or more convolutional layersthat accept residual connections. In addition, after the superblocks,the feature extraction layers 104A and 104B may include anotherconvolutional layer. The output of the feature extraction layers 104Aand 104B may include feature map tensors having dimensions of ½H×½ W×F,where F is the number of features (e.g., without limitation, F may beequal to 32).

The feature extraction layers 104A and 104B may share weights betweencorresponding layers. For example, a second layer of the featureextraction layers 104A may share weights and/or learn the weights (e.g.,during training of the DNN 100) with a second layer of the featureextraction layers 104B.

The DNN 100 may include one or more cost volume layers 106A and 106B.The cost volume layers 106A and 106B may represent or be a result ofconcatenating the first feature map with the second feature map. Forexample, the cost volume layers 106A may correspond to or represent afirst cost volume corresponding to the first image 102A and the costvolume layers 106B may correspond to or represent a second cost volumecorresponding to the second image 102B. For example, to generate thefirst cost volume, the left feature map may be matched against the rightfeature map by sliding the right feature map tensor (e.g., correspondingto the right feature map) to the left along the epipolar lines of theleft feature map tensor (e.g., corresponding to the left feature map).In some examples, the sliding of the right feature map tensor to theleft along the epipolar lines of the left feature map tensor may beafter padding the left feature map tensor by the max disparity. The maxdisparity may be a hyper-parameter of the DNN 100, and may be, in somenon-limiting examples, 96. At corresponding pixel positions between theleft feature map tensor and the right feature map tensor, the leftfeature map and the right feature map may be concatenated and copiedinto a resulting four-dimensional (4D) cost volume (e.g., the first costvolume). In some examples, the 4D cost volume may have dimensions of½D×½H×½ W×2F, where D is the max disparity (e.g., where the spatialdimension of the input images 102 and the max disparity are down-sampledto half size). However, this is not intended to be limiting, and in someexamples, the dimensions may be down-sampled to two-thirds, a quarter,an eighth, and/or by another amount, without departing from the scope ofthe present disclosure.

Similarly, for example, to generate the second cost volume, the rightfeature map may be matched against the left feature map by sliding theleft feature map tensor to the right along the epipolar lines of theright feature map tensor. In some examples, the sliding of the leftfeature map tensor to the right along the epipolar lines of the rightfeature map tensor may be after padding the right feature map tensor bythe max disparity. At corresponding pixel positions between the rightfeature map tensor and the left feature map tensor, the right featuremap and the left feature map may be concatenated and copied into aresulting four-dimensional (4D) cost volume (e.g., the second costvolume). The 4D cost volume may have dimensions of ½D×½H×½ W×2F, where Dis the max disparity (e.g., where the spatial dimension of the inputimages 102 and the max disparity are down-sampled to half size).However, this is not intended to be limiting, and in some examples, thedimensions may be down-sampled to two-thirds, a quarter, an eighth,and/or by another amount, without departing from the scope of thepresent disclosure.

The DNN 100 may include one or more matching layers 108A and 108B. Thematching layers 108A and 108B may be used to perform stereo matching bycomparing features from the cost volumes to determine pixels from thefirst image 102A that match pixels from the second image 102B. Thematching layers 108A and 108B may include an encoder/decoder“bottleneck” framework to allow the DNN 100 to perform matching offeatures at multiple resolutions (e.g., each layer of the encoder—e.g.,a multiscale encoder—may down-sample to a lower resolution forperforming matching). The encoder layers of the matching layers 108A and108B may be followed by decoder layers with skip connections toincorporate information from the various resolutions of the encoderlayers.

In some examples, the matching layers 108A and 108B may include 3Dconvolutional layers followed by deconvolutional layers. In suchexamples, each of the convolutional layers of the matching layers 108Aand 108B may include 3D convolutional layers. However, in some examples,one or more 3D convolutional layers may be followed by one or more 2Dconvolutional layers. For a non-limiting example, two or three 3Dconvolutional layers may be used, followed by 2D convolutional layer(s).By using one or more 2D convolutional layers (e.g., where conventionalsystems may have used all 3D convolutional layers), and thus reducingthe number of 3D convolutional layers, less processing may be requiredwithout sacrificing accuracy. For example, because one or more 3Dconvolutional layers may be still be used, the benefits of the 3Dconvolutional layers may be realized, and using the 2D convolutionallayers may serve to reduce the size of the DNN 100 and the computerequirements for the DNN 100.

The use of the term deconvolutional may be misleading and is notintended to be limiting. For example, the deconvolutional layer(s) mayalternatively be referred to as transposed convolutional layers orfractionally strided convolutional layers. The deconvolutional layer(s)may be used to perform up-sampling on the output of a prior layer. Forexample, the deconvolutional layer(s) may be used to up-sample to aspatial resolution that is equal to the spatial resolution after thedown-sampling layer of the feature extraction layers 104A and 104B(e.g., to ½H×½ W), or otherwise used to up-sample to the input spatialresolution of a next layer (e.g., the next matching layer 108A and 108Bor the up-sampling layer 110A or 110B.

In addition to, or alternatively from, using deconvolutional layersafter the convolutional layers of the matching layers 108A and 108B, oneor more up-sampling layers (e.g., nearest neighbor) may be used. In suchexamples, the up-sampling layer(s) may be followed by one or moreconvolutional layers. As such, in some examples, the matching layers108A and 108B may include encoder layers (e.g., one or more 3Dconvolutional layers and/or one or more 2D convolutional layers)followed by decoder layers that are either deconvolutional layers or acombination of up-sampling and convolutional layers.

In some examples, the matching layers 108A and 108B may include fourdown-sampling layers, followed by two convolutional layers, followed byfour up-sampling layers that accept residual connections. In someexamples, the matching layers 108A and 108B may share weights betweencorresponding layers. For example, a first layer of the matching layers108A may share weights and/or learn the weights (e.g., during trainingof the DNN 100) with a first layer of the matching layers 108B.

After the last decoder layer (e.g., deconvolutional layer) of thematching layers 108A and 108B, there may be one or more up-samplinglayers 110A and 110B. The up-sampling layer(s) may be used to produce aleft tensor corresponding to the left image (e.g., the first image 102A)and a right tensor corresponding to the right image (e.g., the secondimage 102B) having dimensions D×H×W×1. The left tensor and the righttensor may be representative of matching costs between pixels of thefirst image 102A and pixels of the second image 102B. In some examples,without limitation, the DNN 100 may include only one up-sampling layer110A and 110B.

The DNN 100 may further include one or more aggregator layers 112A and112B. In some examples, the aggregator may be a soft argmax functionthat may use the costs from the output of the up-sampling layer(s) 110(e.g., after conversion to probabilities) to determine the best or mostlikely disparity for each pixel of each of the first image 102A and thesecond image 102B. However, soft argmax has the drawback of assumingthat all context has already been taken into account, which is notalways the case. As a result, in some examples, the DNN 100 may includethe machine learned (ML) argmax function. The ML argmax function maynormalize the probability volume (e.g., where the probabilities are thecosts after conversion) across the disparity dimension, D. The ML argmaxfunction may include one or more convolutional layers (e.g., the MLargmax layers 112A and 112B) that may produce a single value for eachpixel. By using one or more convolutional layers to implement the MLargmax layers function, the ML argmax layers 112A and 112B may betrained to learn the context—something that was not possible with softargmax function. As such, the ML argmax layer(s) 112A and 112B mayinclude trainable parameters (e.g., weights and biases) that may betrained using backpropagation during training of the DNN 100 (e.g., theML argmax function may be referred to as a parametrized version of thesoft argmax function).

In some examples, the ML argmax layers 112A and 112B may include only(2D and/or 3D) convolutional layers. In other examples, the ML argmaxlayers 112A and 112B may include an encoder/decoder framework includingconvolutional layers followed by deconvolutional layers. By including anencoder/decoder (e.g., bottleneck) framework, the DNN 100 may requireless compute requirements, and may increase training times for the DNN100. The ML argmax layers 112A and 112B may include five layers in somenon-limiting examples, where the layers may be (2D and/or 3D)convolutional layers, deconvolutional layers, or a combination thereof.

The DNN 100 may apply a sigmoid activation function 114A and 114B at theoutput of the ML argmax layers 112A and 112B. The sigmoid activationfunction 114A and 114B may convert the value for each pixel output bythe ML argmax layers 112A and 112B to a disparity estimate for thepixel. When the sigmoid activation functions 114A and 114B are not used,the disparity value estimates may be less accurate.

As a result of the combination of the ML argmax layers 112A and 112B andthe sigmoid activation functions 114A and 114B, the disparity valuesextracted are more accurate. In addition, the DNN 100, and specificallythe ML argmax layers 112A and 112B, are better at handling uniform ormulti-modal probability distributions than soft argmax. The ML argmaxfunction (implemented using the ML argmax layers 112A and 112B) may alsoyield more stable convergence during training of the DNN 100.

In some example, the DNN 100 may include rectified linear unit (ReLU)activation functions (e.g., applied to or represented by one or morelayers of the DNN 100). The ReLU activation function may apply anelementwise activation function, such as the max (0, x), thresholding atzero, for example. The resulting volume of a ReLU layer may be the sameas the volume of the input of the ReLU layer. When using a ReLUactivation function, however, batch normalization layers may need to beincluded (e.g., on outputs of convolutional layers prior to being inputto the ReLU activation function), thereby increasing the overall size ofthe DNN 100.

In some examples, in order to reduce the size and thus the run-time forthe DNN 100, and further to reduce processing requirements for executingthe DNN 100, ReLU activation functions and batch normalization layersmay not be used. In such examples, exponential linear unit (ELU)activation functions may be used. ELU activation functions may includenegative values which may allow the ELU activation function to push meanunit activations closer to zero. By pushing the mean unit activationscloser to zero, learning rates may be increased (comparatively to ReLUwith batch normalization) because the gradient is brought closer to unitnatural gradient. ReLU activation functions (by not including negativevalues) rely on batch normalization layers to push the mean towardszero, while ELU activation functions are able to accomplish this withoutbatch normalization layers and thus with a smaller computationalfootprint. As such, in some examples, ELU activation functions may beused, and ReLU activation functions and batch normalization layers maynot.

In some examples, such as described herein, layers of the DNN 100 mayinclude parameters (e.g., weights and/or biases), while others may not,such as the ELU layers, for example. The parameters may be learned bythe DNN 100 during training. Further, some of the layers of the DNN mayinclude additional hyper-parameters (e.g., learning rate, stride,epochs, kernel size, number of filters, max disparity, etc.)—such as theconvolutional layer(s) and the deconvolutional layer(s)—while otherlayers may not, such as the ReLU or ELU layer(s). Although ReLU, ELU,and sigmoid activation functions are described herein, variousactivation functions may be used, including but not limited to, leakyReLU, parametric ReLU, linear, hyperbolic tangent (tan h), etc. Theparameters, hyper-parameters, and/or activation functions are not to belimited and may differ depending on the embodiment.

In some examples, the DNN 100 may be trained for 75,000, 85,000, 90,000or more iterations (e.g., between approximately 2 and 3 epochs), with abatch size of 1. In addition, an optimizer may be used in some examples,such as an Adam optimizer. In other examples, gradient descent orstochastic gradient descent may be used. The learning rate may be, insome examples, 10⁻⁴, which may be reduced over time.

The disparity values predicted by the DNN 100 may be used to determinedepth of the features in the physical environment represented by thepixels of the images 102. For example, using equation (1), above, thedisparity values may be used to calculate a distance, or depth, of theobjects in the physical environment from the vehicle 500 (or otherobject, such as a drone, robot, etc.). In addition, the calibration ofthe cameras (or other sensors of the vehicle 500) may includecorrelations between pixel positions and x, y coordinates in thephysical environment. As such, by also understanding depth, accurate 3Dcoordinates of objects in the physical environment may be determined(e.g., an x, y, and z location). The location of the objects may be usedby the vehicle 500 (e.g., one or more components of the autonomousdriving software stack) or another object to aid in navigating orotherwise understanding the physical environment.

Now referring to FIG. 1C, FIG. 1C is a table 134 comparing accuracy ofdifferent architectures for machine learning models, in accordance withsome embodiments of the present disclosure. For example, the table 134may include D1-all error measures corresponding to the models in themodel column 122 of the table 120. The data in the table 134 wasgenerated using the models and the associated architectures asillustrated in the table 120. The training data used for generating thedata was a combination of LIDAR data in supervised training andphotometric consistency in unsupervised training. The use of thissemi-supervised combination is described in more detail herein whencomparing supervised, unsupervised, and unsupervised training methods.More specifically, the training data was 200 KITTI 2015 augmentedtraining images and 29,000 KITTI images with sparse ground truth data.The network size in size column 138 may correspond to the number ofweights used (e.g., 3.1 M represents 3.1 million weights).

As a result of the testing, the ML argmax model performed the best(e.g., had the least D1-all error) as compared to the other models, asindicated by the table 134. In addition, reducing the size of thenetwork by either using a smaller network, using cross-correlation, orby removing one of the towers entirely only had a slight effect on theerror, despite the fact that a single tower requires 1.8 times lessmemory, using cross-correlation requires 64× less memory, the smallnetwork contains 36% fewer weights, and the tiny network contain 82%fewer weights. This data from table 134 is thus an indication of theimportance and effectiveness of using the encoder/decoder bottleneckframework in the matching layers 108A and 108B of the DNN 100 to extractinformation from the cost volumes. The data further indicates thatconcatenation, as compared to cross-correlation, is more accurate.

In some examples, the DNN 100 may be implemented on an embedded graphicsprocessing unit (GPU). By using an embedded GPU, programmaticoptimization may be more achievable. In addition, the DNN 100 may bemore capable of real-time (e.g., 20 frames per second) deployment whenusing an embedded GPU, especially where 3D convolutional layers and/or2D convolutional layers are used. For example, with reference to FIG.1D, computation times (in milliseconds) for different stereo DNN models(e.g., as indicated in model column 144 of table 142) on various GPUarchitectures (e.g., NVIDIA Titan XP 148, NVIDIA GTX 1060 150, and anembedded NVIDIA Jetson TX2 152). Resolution column 146 indicates theimage dimensions and the max disparity (e.g., H×W×D). The columnslabeled “TF” indicate TensorFlow runtime and the columns labeled “opt”indicate custom runtime based on TensorRT/cuDNN. The custom runtime mayinclude a set of custom plugins for TensorRT that may implement the 3Dconvolutions/deconvolutions (e.g., of the matching layers 108A and 108Bof the DNN 100), the cost volume creation, soft argmax, and ELUactivation functions. The single “OOM” indicates an “out of memory”exception. For the embedded Jetson TX2 152, only the custom runtime,opt, was used because TensorFlow is not compatible. As indicated by thetable 142, by using a custom runtime with Titan XP, near real-timeperformance (e.g., 20 fps) was achieved, while efficient performance wasachieved on the embedded Jetson TX2 152.

Now referring to FIG. 2, each block of method 200, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, method 200 isdescribed, by way of example, with respect to the DNN 100 of FIG. 1.However, this method may additionally or alternatively be executed byany one system, or any combination of systems, including, but notlimited to, those described herein.

FIG. 2 is a flow diagram illustrating an example method 200 forpredicting depth from stereoscopic images using a machine learningmodel, in accordance with some embodiments of the present disclosure.The method 200, at block B202, includes determining a first cost volumeand a second volume. For example, a first cost volume (represented bythe cost volume layer(s) 106A) and the second cost volume (representedby the cost volume layer(s) 106B) may be determined. The determinationmay include concatenating the left feature map tensor with the rightfeature map tensor to generate the first cost volume and theconcatenating the right feature map tensor with the left feature maptensor to generate the second cost volume. The left feature map may becomputed based on image data representing the first image 102A and theright feature map may be computed based on image data representing thesecond image 102B. As such, the determination of the first cost volumeand the second volume may be based at least in part on one or morecomparisons between image data representative of the first image 102A ofa first field of view of a first sensor (e.g., a first or left imagesensor of a stereo camera) and image data representative of a secondimage 102B of a second field of view of a second sensor (e.g., a secondor right image sensor of the stereo camera).

The method 200, at block B204, includes applying the first cost volumeand the second cost volume to matching layers of a machine learningmodel. For example, the first cost volume may be applied to the matchinglayers 108A and the second volume may be applied to the matching layers108B of the DNN 100.

The method 200, at block B206, includes computing, by the matchinglayers, matching costs. For example, the matching layers 108A and 108Bmay compute matching costs between features of the images 102.

The method 200, at block B208, includes applying the matching costs to amachine learned argmax function of the machine learning model. Forexample—and after converting the costs to probabilities in someexamples—the matching costs (or probabilities) may be applied to the MLargmax layers 112A and 112B of the DNN 100.

The method 200, at block B210, includes computing, using the machinelearned argmax function, first disparity values and second disparityvalues. For example, the ML argmax layers 112A may compute firstdisparity values corresponding to the first image 102A and the ML argmaxlayers 112B may compute second disparity values corresponding to thesecond image 102B. In some examples, the first disparity values and thesecond disparity values may be applied to sigmoid activation functions114A and 114B to generate final disparity value predictions.

Training the Stereoscopic Deep Neural Network

As described herein, conventional stereo DNNs have used either LIDARdata alone (supervised training) or photometric consistency alone(unsupervised training) to train the stereo DNNs. However, both LIDARdata and photometric consistency have drawbacks. For example, asdescribed herein with respect to conventional DNN training, using LIDARdata often results in noisy outputs (e.g., due to sparsity in data) thathave reduced sharpness along edges of objects, and that suffer atgreater distances. In addition, LIDAR data has a limited field of view(e.g., 2 degrees upward and 35 degrees downward). LIDAR sensor may alsoproduce lines that are represented in the LIDAR data, and that requirefiltering and/or post-processing to remove. However, by filtering and/orpost-processing, the LIDAR data is even sparser and thus results inincreased noise. Photometric consistency may also include drawbacks,such as noise in the output as a result of inaccuracy of cameracalibration, movement of the camera during image capture, etc. Inaddition, by being completely unsupervised, the accuracy of the groundtruth may suffer, whereas with supervised training the accuracy of theground truth may be validated.

As a result of the drawbacks of conventional training methods for stereoDNNs, the stereo DNNs of the present disclosure may use both LIDAR data(supervised training mode) and photometric consistency (unsupervisedtraining mode) during training in a semi-supervised mode or manner Byusing both photometric consistency and LIDAR data, the benefits of eachmay be learned by a DNN while the drawbacks to each may be mitigated bythe other. For example, by using photometric error in addition to LIDARdata, accuracy at greater distances may be increased and the sharpnessalong edges of objects may be increased. In particular, usingphotometric error may decrease the noise of LIDAR data only training byincreasing the amount of data that can be used for predicting depth ascompared to the sparse data from LIDAR alone. In addition, by includingphotometric consistency, the field of view of the training data may beincreased as compared to LIDAR data alone, thus resulting in depthdeterminations by the DNN for a larger portion of the physicalenvironment.

Now referring to FIG. 3A, FIG. 3A is a data flow diagram illustrating aprocess 300 for training a machine learning model for depth estimationusing stereoscopic images, in accordance with some embodiments of thepresent disclosure. The process 300 may include using training images(e.g., the first image(s) 102A and the second image(s) 102B) to trainthe DNN 100 to generate accurate and acceptable results (e.g., predicteddisparity values). The DNN 100 may be capable of being trained usingLIDAR data in a supervised manner, photometric consistency in anunsupervised manner, or a combination thereof.

As described herein, the training data may include LIDAR data 304 forsupervised training, where depth values from the LIDAR data 304 are usedas ground truth values for comparison to the disparity values from theoutput of the DNN 100. For example, the depth data from the LIDAR data304 may be projected into the first image 102A and/or the second image102B, and then disparity values may be assigned to the pixels of thefirst image 102A and/or the second image 102B using the LIDAR data 304.The LIDAR data 304 may be generated by one or more LIDAR sensors ofvehicles, or other objects, captured in a real-world physicalenvironment and/or may include simulated or virtual data from virtualLIDAR sensors of a virtual vehicle, or other object, in a virtualsimulation.

The training data may further include using photometric consistency bywarping the images 102 based on the predicted disparity values (e.g.,from the disparity maps 116). For example, a structure similaritymetric, S_(sim), may be determined based on a comparison of the firstimage 102A and the second image 102B using one of the first disparitymap 116A or the second disparity map 116B. As an example using the firstimage 102A, the pixels of the first image 102A may be warped to coincidewith the second camera (e.g., the camera or image sensor that capturedthe second image 102B) with the values from the first disparity map116A. As such, the first image 102A may be converted to the pixels orimage plane of the second camera, shifted by the disparity, and then acomparison may be calculated as S_(sim) between the pixels of the warpedand shifted first image 102A and the second image 102B. The same processmay be used for the second image 102B with respect to the first camera,where the second image 102B may be converted to the pixels or imageplane of the first camera, shifted by the disparity (e.g., from thesecond disparity map 116B), and then a comparison may be calculated asS_(sim) between the pixels of the warped and shifted second image 102Band the first image 102A. In any example, a larger value of S_(sim) mayindicate a greater error. Back propagation may be used during trainingto update the parameters of the DNN 100 until the values of S_(sim) areless, or within an acceptable accuracy range.

In some examples, the DNN 100 may be trained using multiple iterationsuntil the value of a loss function(s) 302 of the DNN 100 is below athreshold loss value. For example, the DNN 100 may perform forward passcomputations on the images 102. In some examples, the DNN 100 mayextract features of interest from the images 102 and predict disparityvalues (e.g., as represented by the disparity maps 116A and 116B) on apixel-by-pixel basis. The loss function(s) 302 may be used to measureerror in the predictions of the DNN 100 using ground truth data, asdescribed in more detail herein.

The loss function 302, in some examples, may include an L1 lossfunction. However, this is not intended to be limiting and other lossfunctions may be used without departing form the scope of the presentdisclosure (e.g., cross entropy, L2 loss, etc.) Backward passcomputations may be performed to recursively compute gradients of theloss function 302 with respect to training parameters. In some examples,weights and biases of the DNN 100 may be used to compute thesegradients.

In one non-limiting example, the loss function 302 may combine thesupervised term along with an unsupervised term, as represented inequation (2), below:

$\begin{matrix}{L = {{\lambda_{1}E_{image}} + {\lambda_{2}E_{LIDAR}} + {\lambda_{3}E_{lr}} + {\lambda_{4}E_{ds}\mspace{14mu} {where}}}} & (2) \\{E_{image} = {E_{image}^{l} + E_{image}^{l}}} & (3) \\{E_{LIDAR} = {{{d_{l} - {\overset{\_}{d}}_{l}}} + {{d_{r} - {\overset{\_}{d}}_{r}}}}} & (4) \\{E_{lr} = {{\frac{1}{n}{\sum\limits_{ij}{{d_{ij}^{l} - {\overset{\sim}{d}}_{ij}^{l}}}}} + {\frac{1}{n}{\sum\limits_{ij}{{d_{ij}^{r} - {\overset{\sim}{d}}_{ij}^{r}}}}}}} & (5) \\{E_{ds} = {E_{ds}^{l} + E_{ds}^{r}}} & (6)\end{matrix}$

where equation (2) may ensure photometric consistency, equation (3) maycompare estimated disparities to sparse LIDA data, equation (4) mayensure that the first disparity map 116A and the second disparity map116B are consistent with each other, and equation (5) may encourage thedisparity maps 116 to be piecewise smooth. In addition, E^(l) _(image),E^(l) _(ds), E^(l′) _(image), and E^(r) _(ds) may be representedaccording to equations (7)-(10), below:

$\begin{matrix}{E_{image}^{l} = {{\frac{1}{n}{\sum\limits_{i,j}{\alpha \frac{1 - {S_{sim}\left( {I_{ij}^{l},{\overset{\sim}{I}}_{ij}^{l}} \right.}}{2}}}} + {\left( {1 - \alpha} \right){{I_{ij}^{l} - {\overset{\sim}{I}}_{ij}^{l}}}}}} & (7) \\{E_{ds}^{l} = {{\frac{1}{n}{\sum\limits_{i,j}{{{\partial_{x}d_{ij}^{l}}}e^{- {{\partial_{x}I_{i,j}^{l}}}}}}} + {{{\partial_{y}d_{ij}^{l}}}e^{- {{\partial_{y}I_{i,j}^{l}}}}}}} & (8) \\{E_{image}^{r} = {{\frac{1}{n}{\sum\limits_{i,j}{\alpha \frac{1 - {S_{sim}\left( {I_{ij}^{r},{\overset{\sim}{I}}_{ij}^{r}} \right.}}{2}}}} + {\left( {1 - \alpha} \right){{I_{ij}^{r} - {\overset{\sim}{I}}_{ij}^{r}}}}}} & (9) \\{E_{ds}^{r} = {{\frac{1}{n}{\sum\limits_{i,j}{{{\partial_{x}d_{ij}^{r}}}e^{- {{\partial_{x}I_{i,j}^{r}}}}}}} + {{{\partial_{y}d_{ij}^{r}}}e^{- {{\partial_{y}I_{i,j}^{r}}}}\mspace{14mu} {where}}}} & (10) \\{{\overset{\sim}{I}}^{l} = {w_{rl}\left( {I_{r},d_{l}} \right)}} & (11) \\{{\overset{\sim}{I}}^{r} = {w_{lr}\left( {I_{l},d_{r}} \right)}} & (12) \\{{\overset{\sim}{d}}^{l} = {w_{rl}\left( {d_{r},d_{l}} \right)}} & (13) \\{{\overset{\sim}{d}}^{r} = {w_{lr}\left( {d_{l},d_{r}} \right)}} & (14) \\{{w_{lr}\left( {I,d} \right)} = \left. \left( {x,y} \right)\mapsto{I\left( {{x - {d\left( {x,y} \right)}},y} \right)} \right.} & (15) \\{{w_{rl}\left( {I,d} \right)} = \left. \left( {x,y} \right)\mapsto{I\left( {{x + {d\left( {x,y} \right)}},y} \right)} \right.} & (16) \\{{S_{sim}\left( {x,y} \right)} = {\left( \frac{{2\mu_{x}\mu_{y}} + c_{1}}{\mu_{x}^{2} + \mu_{y}^{2} + c_{1}} \right)\left( \frac{{2\sigma_{xy}} + c_{2}}{\sigma_{x}^{2} + \sigma_{y}^{2} + c_{2}} \right)}} & (17)\end{matrix}$

where I_(l) is the first image 102A and I_(r) is the second image 102B,dr is the first disparity map 116A and dr is the second disparity map116B, d _(l) and d _(r) are the ground truth disparity mapscorresponding to the first disparity map 116A and the second disparitymap 116B, respectively, S_(sim) is the structural similarity index, n isthe number of pixels, and c₁ and c₂ are constants to avoid dividing byzero. In some examples, c₁=10⁻⁴ and c₂=10⁻³. In some examples, such asin equations (15) and (16), the coordinates may be non-integers, inwhich case bilinear interpolation may be used.

In some examples, an optimizer may be used to make adjustments to thetraining parameters (e.g., weights, biases, etc.), as described herein.In one non-limiting example, an Adam optimizer may be used, while inother examples, stochastic gradient descent, stochastic gradient descentwith a momentum term, and/or another optimizer may be used. The trainingprocess may be reiterated until the trained parameters converge tooptimum, desired, and/or acceptable values.

In some examples, a trained or deployed stereoscopic DNN, such as theDNN 100, may be used to train a monocular DNN. For example, becausemonocular DNNs may not be as accurate as stereoscopic DNNs, the outputsof the stereoscopic DNNs may be used as ground truth for training amonocular DNN. For example, a monocular DNN may use a single input imageof a field of view of a real-world environment, and stereo images may becaptured at the same time of at least a portion of the same field ofview. As such, the disparity maps output by the stereoscopic DNNs may beused as ground truth to train the monocular DNN. In such examples, themonocular DNN may learn to more accurately predict more objects, withmore context, than a monocular DNN being trained on monocular dataalone.

Now referring to FIG. 3B, FIG. 3B is a table 306 comparing accuracy ofdifferent training methods for machine learning models, in accordancewith some embodiments of the present disclosure. For example, differentmodels (as indicated in the models column 308)—such as conventionalmonocular models (e.g., monoDepth)—were tested in addition to differentvariations of the DNN 100. The training data used to compute the resultsin table 306 may be the same training data used for the table 134 ofFIG. 1C, described herein. The table 306 includes D1-all error for eachof the models (except for monoDepth due to limitations on types oftraining data that can be used for monocular networks) using LIDAR dataonly (e.g., indicated by the LIDAR column 310), photometric consistencyonly (e.g., indicated by the photometric column 312), and a combinationof LIDAR and photometric (e.g., indicated by the LIDAR and photometriccolumn 314). The data indicates that a monocular DNN, such as monoDepth,is much less accurate than using a stereoscopic DNN, such as the DNN100. In addition, the data indicates that the lowest error, and thus thebest accuracy, is accomplished by using LIDAR data and photometricconsistency in semi-supervised training, as described herein. As such,the table 306 provides testing validation of the benefits of stereo DNNsover monocular DNNs, as well as the improvements in accuracy from usingsemi-supervised training as opposed to supervised or unsupervised only.

Now referring to FIG. 3C, FIG. 3C includes an illustration of exampledisparity maps 318 based on outputs machine learning models, inaccordance with some embodiments of the present disclosure. For example,FIG. 3C highlights advantages of using semi-supervised training (e.g.,LIDAR data and photometric consistency) as compared to supervised orunsupervised approaches. FIG. 5C may include an image 316 of a dividedhighway including a guardrail 322, multiple vehicles, trees, the sky,and other features in the environment. Disparity map 318A is a grayscaleconversion of a disparity map generated by a stereo DNN trained usingLIDAR data only (e.g., supervised training), disparity map 318B is agrayscale conversion of a disparity map generated by a stereo DNNtrained using photometric consistency only (e.g., unsupervisedtraining), and disparity map 318C is a grayscale conversion of adisparity map generated by a stereo DNN trained using LIDAR data andphotometric consistency together (e.g., semi-supervised training) Thedisparity map 318A does not include the guardrail 322. This may be aresult of the sparsity of LIDAR data, in addition to other drawbacks ofusing LIDAR data—such as the need for post-processing and filtering. Thedisparity map 318B includes the guardrail 322, but is noisier and losessome smoothness along the edges of the vehicles. The noise in thedisparity map 318B may result in sharper or harsher contrast betweenadjacent pixels of similar depth where there shouldn't be, therebyreducing the accuracy of the disparity map 318B. The disparity map 318Cincludes the guardrail 322, and includes less noise while maintainingthe smoothness along the vehicle and other features of the environment.Disparity scale 320 represents the disparity values that correspond tothe colors or gradients of the image 316.

Although the described differences with respect to FIG. 3C may bedifficult to see in the grayscale conversions of the disparity maps 318,FIG. 5 of U.S. Provisional Application No. 62/646,148, filed on Mar. 21,2018, which is hereby incorporated by reference in its entirety,includes a more accurate representation in full-color.

Now referring to FIG. 4, each block of method 400, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, method 400 isdescribed, by way of example, with respect to the process 300 of FIG.3A. However, the method may additionally or alternatively be executed byany one system, or any combination of systems, including, but notlimited to, those described herein.

FIG. 4 is a flow diagram illustrating an example method 400 for traininga machine learning model to predict depth from stereoscopic images, inaccordance with some embodiments of the present disclosure. The method400, at block B402, includes receiving first image data generated duringa unit of time and second image data generated during the same unit oftime. For example, a stereo camera may capture the first image 102A andthe second image 102B at a same time, and the first image 102A and thesecond image 102B may be received for use in training the DNN 100.

The method 400, at block B404, includes applying the first image dataand the second image data to a neural network. For example, first imagedata representative of the first image 102A and second image datarepresentative of the second image 102B may be applied to (or input to)the DNN 100.

The method 400, at block B406, includes computing, by the neuralnetwork, a first disparity map and a second disparity map. For example,the DNN 100 may compute the first disparity map 116A and the seconddisparity map 116B.

The method 400, at block B408, includes computing a first loss based atleast in part on the comparing a first image and a second image using atleast one of the first disparity map and the second disparity map. Forexample, the first image 102A may be compared to the second image 102Busing the first disparity map 116A, and the second image 102B may becompared to the first image 102A using the second disparity map 116B.

The method 400, at block B410, includes receiving LIDAR data generatedat the same unit of time. For example, the LIDAR data 304 may bereceived that was generated at the same time as the first image 102A andthe second image 102B.

The method 400, at block B412, includes computing a second loss based atleast in part on comparing the LIDAR data to at least one of the firstdisparity map or the second disparity map. For example, the LIDAR data304 may be compared to the first disparity map 116A and/or the seconddisparity map 116B to compute a second loss.

The method 400, at block B414, includes updating the one or moreparameters of the neural network based at least in part on the firstloss and the second loss. For example, the parameters of the layers ofthe DNN 100 may be trained using the loss function 302, where the lossfunction 302 may be a combination of the LIDAR data loss and thephotometric consistency loss.

Example Autonomous Vehicle

FIG. 5A is an illustration of an example autonomous vehicle 500, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 500 (alternatively referred to herein as the “vehicle500”) may include a passenger vehicle, such as a car, a truck, a bus,and/or another type of vehicle that accommodates one or more passengers.Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 500 may be capable offunctionality in accordance with one or more of Level 3-Level 5 of theautonomous driving levels. For example, the vehicle 500 may be capableof conditional automation (Level 3), high automation (Level 4), and/orfull automation (Level 5), depending on the embodiment.

The vehicle 500 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 500 may include a propulsion system550, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 550 may be connected to a drive train of the vehicle500, which may include a transmission, to enable the propulsion of thevehicle 500. The propulsion system 550 may be controlled in response toreceiving signals from the throttle/accelerator 552.

A steering system 554, which may include a steering wheel, may be usedto steer the vehicle 500 (e.g., along a desired path or route) when thepropulsion system 550 is operating (e.g., when the vehicle is inmotion). The steering system 554 may receive signals from a steeringactuator 556. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 546 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 548 and/or brakesensors.

Controller(s) 536, which may include one or more system on chips (SoCs)504 (FIG. 5C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle500. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 548, to operate thesteering system 554 via one or more steering actuators 556, to operatethe propulsion system 550 via one or more throttle/accelerators 552. Thecontroller(s) 536 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 500. The controller(s) 536 may include a first controller 536for autonomous driving functions, a second controller 536 for functionalsafety functions, a third controller 536 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 536 forinfotainment functionality, a fifth controller 536 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 536 may handle two or more of the abovefunctionalities, two or more controllers 536 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 536 may provide the signals for controlling one ormore components and/or systems of the vehicle 500 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 558 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 560, ultrasonic sensor(s) 562, LIDARsensor(s) 564, inertial measurement unit (IMU) sensor(s) 566 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 596, stereo camera(s) 568, wide-view camera(s) 570(e.g., fisheye cameras), infrared camera(s) 572, surround camera(s) 574(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 598,speed sensor(s) 544 (e.g., for measuring the speed of the vehicle 500),vibration sensor(s) 542, steering sensor(s) 540, brake sensor(s) (e.g.,as part of the brake sensor system 546), and/or other sensor types.

One or more of the controller(s) 536 may receive inputs (e.g.,represented by input data) from an instrument cluster 532 of the vehicle500 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 534, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle500. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 522 of FIG. 5C), location data(e.g., the vehicle's 500 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 536,etc. For example, the HMI display 534 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 500 further includes a network interface 524 which may useone or more wireless antenna(s) 526 and/or modem(s) to communicate overone or more networks. For example, the network interface 524 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 526 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 5B is an example of camera locations and fields of view for theexample autonomous vehicle 500 of FIG. 5A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle500.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 500. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 520 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 500 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 536 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 570 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.5B, there may any number of wide-view cameras 570 on the vehicle 500. Inaddition, long-range camera(s) 598 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 598 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 568 may also be included in a front-facingconfiguration. The stereo camera(s) 568 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 568 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 568 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 500 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 574 (e.g., four surround cameras 574 asillustrated in FIG. 5B) may be positioned to on the vehicle 500. Thesurround camera(s) 574 may include wide-view camera(s) 570, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 574 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 500 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 598,stereo camera(s) 568), infrared camera(s) 572, etc.), as describedherein.

FIG. 5C is a block diagram of an example system architecture for theexample autonomous vehicle 500 of FIG. 5A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 500 in FIG.5C are illustrated as being connected via bus 502. The bus 502 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 500 used to aid in control of various features and functionalityof the vehicle 500, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 502 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 502, this is notintended to be limiting. For example, there may be any number of busses502, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses502 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 502 may be used for collisionavoidance functionality and a second bus 502 may be used for actuationcontrol. In any example, each bus 502 may communicate with any of thecomponents of the vehicle 500, and two or more busses 502 maycommunicate with the same components. In some examples, each SoC 504,each controller 536, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle500), and may be connected to a common bus, such the CAN bus.

The vehicle 500 may include one or more controller(s) 536, such as thosedescribed herein with respect to FIG. 5A. The controller(s) 536 may beused for a variety of functions. The controller(s) 536 may be coupled toany of the various other components and systems of the vehicle 500, andmay be used for control of the vehicle 500, artificial intelligence ofthe vehicle 500, infotainment for the vehicle 500, and/or the like.

The vehicle 500 may include a system(s) on a chip (SoC) 504. The SoC 504may include CPU(s) 506, GPU(s) 508, processor(s) 510, cache(s) 512,accelerator(s) 514, data store(s) 516, and/or other components andfeatures not illustrated. The SoC(s) 504 may be used to control thevehicle 500 in a variety of platforms and systems. For example, theSoC(s) 504 may be combined in a system (e.g., the system of the vehicle500) with an HD map 522 which may obtain map refreshes and/or updatesvia a network interface 524 from one or more servers (e.g., server(s)578 of FIG. 5D).

The CPU(s) 506 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 506 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 506may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 506 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 506 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)506 to be active at any given time.

The CPU(s) 506 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 506may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 508 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 508 may be programmable and may beefficient for parallel workloads. The GPU(s) 508, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 508 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 508 may include at least eight streamingmicroprocessors. The GPU(s) 508 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 508 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 508 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 508 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 508 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an LO instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 508 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 508 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 508 to access the CPU(s) 506 page tables directly. Insuch examples, when the GPU(s) 508 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 506. In response, the CPU(s) 506 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 508. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 506 and the GPU(s) 508, thereby simplifying the GPU(s) 508programming and porting of applications to the GPU(s) 508.

In addition, the GPU(s) 508 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 508 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 504 may include any number of cache(s) 512, including thosedescribed herein. For example, the cache(s) 512 may include an L3 cachethat is available to both the CPU(s) 506 and the GPU(s) 508 (e.g., thatis connected both the CPU(s) 506 and the GPU(s) 508). The cache(s) 512may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 504 may include one or more accelerators 514 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 504 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 508 and to off-load some of the tasks of theGPU(s) 508 (e.g., to free up more cycles of the GPU(s) 508 forperforming other tasks). As an example, the accelerator(s) 514 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 508, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 508 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 508 and/or other accelerator(s) 514.

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 506. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 514. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 504 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real0time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses.

The accelerator(s) 514 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 566 output thatcorrelates with the vehicle 500 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 564 or RADAR sensor(s) 560), amongothers.

The SoC(s) 504 may include data store(s) 516 (e.g., memory). The datastore(s) 516 may be on-chip memory of the SoC(s) 504, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 516 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 512 may comprise L2 or L3 cache(s) 512. Reference to thedata store(s) 516 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 514, as described herein.

The SoC(s) 504 may include one or more processor(s) 510 (e.g., embeddedprocessors). The processor(s) 510 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 504 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 504 thermals and temperature sensors, and/ormanagement of the SoC(s) 504 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 504 may use thering-oscillators to detect temperatures of the CPU(s) 506, GPU(s) 508,and/or accelerator(s) 514. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 504 into a lower powerstate and/or put the vehicle 500 into a chauffeur to safe stop mode(e.g., bring the vehicle 500 to a safe stop).

The processor(s) 510 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 510 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 510 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 510 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 510 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 510 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)570, surround camera(s) 574, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 508 is not required tocontinuously render new surfaces. Even when the GPU(s) 508 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 508 to improve performance and responsiveness.

The SoC(s) 504 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 504 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 504 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 504 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 564, RADAR sensor(s) 560,etc. that may be connected over Ethernet), data from bus 502 (e.g.,speed of vehicle 500, steering wheel position, etc.), data from GNSSsensor(s) 558 (e.g., connected over Ethernet or CAN bus). The SoC(s) 504may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 506 from routine data management tasks.

The SoC(s) 504 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 504 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 514, when combined with the CPU(s) 506, the GPU(s) 508,and the data store(s) 516, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 520) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 508.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 500. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 504 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 596 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 504 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)558. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 562, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 518 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 504 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 518 may include an X86 processor,for example. The CPU(s) 518 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 504, and/or monitoring the statusand health of the controller(s) 536 and/or infotainment SoC 530, forexample.

The vehicle 500 may include a GPU(s) 520 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 504 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 520 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 500.

The vehicle 500 may further include the network interface 524 which mayinclude one or more wireless antennas 526 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 524 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 578 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 500information about vehicles in proximity to the vehicle 500 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 500).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 500.

The network interface 524 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 536 tocommunicate over wireless networks. The network interface 524 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 500 may further include data store(s) 528 which may includeoff-chip (e.g., off the SoC(s) 504) storage. The data store(s) 528 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 500 may further include GNSS sensor(s) 558. The GNSSsensor(s) 558 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 558 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 500 may further include RADAR sensor(s) 560. The RADARsensor(s) 560 may be used by the vehicle 500 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 560 may usethe CAN and/or the bus 502 (e.g., to transmit data generated by theRADAR sensor(s) 560) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 560 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 560 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 560may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 500 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 500 lane.

Mid-range RADAR systems may include, as an example, a range of up to 560m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 550 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 500 may further include ultrasonic sensor(s) 562. Theultrasonic sensor(s) 562, which may be positioned at the front, back,and/or the sides of the vehicle 500, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 562 may be used, and different ultrasonic sensor(s) 562 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 562 may operate at functional safety levels of ASILB.

The vehicle 500 may include LIDAR sensor(s) 564. The LIDAR sensor(s) 564may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 564 maybe functional safety level ASIL B. In some examples, the vehicle 500 mayinclude multiple LIDAR sensors 564 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 564 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 564 may have an advertised rangeof approximately 500 m, with an accuracy of 2 cm-3 cm, and with supportfor a 500 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 564 may be used. In such examples,the LIDAR sensor(s) 564 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 500.The LIDAR sensor(s) 564, in such examples, may provide up to a520-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)564 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 500. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)564 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 566. The IMU sensor(s) 566may be located at a center of the rear axle of the vehicle 500, in someexamples. The IMU sensor(s) 566 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 566 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 566 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 566 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 566 may enable the vehicle 500to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 566. In some examples, the IMU sensor(s) 566 and theGNSS sensor(s) 558 may be combined in a single integrated unit.

The vehicle may include microphone(s) 596 placed in and/or around thevehicle 500. The microphone(s) 596 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 568, wide-view camera(s) 570, infrared camera(s) 572,surround camera(s) 574, long-range and/or mid-range camera(s) 598,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 500. The types of cameras useddepends on the embodiments and requirements for the vehicle 500, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 500. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 5A and FIG. 5B.

The vehicle 500 may further include vibration sensor(s) 542. Thevibration sensor(s) 542 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 542 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 500 may include an ADAS system 538. The ADAS system 538 mayinclude a SoC, in some examples. The ADAS system 538 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 560, LIDAR sensor(s) 564, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 500 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 500 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 524 and/or the wireless antenna(s) 526 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 500), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 500, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 560, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 560, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle500 crosses lane markings A LDW system does not activate when the driverindicates an intentional lane departure, by activating a turn signal.LDW systems may use front-side facing cameras, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 500 if the vehicle 500 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)560, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 500 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 560, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 500, the vehicle 500itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 536 or a second controller 536). For example, in someembodiments, the ADAS system 538 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 538may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 504.

In other examples, ADAS system 538 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 538 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 538indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 500 may further include the infotainment SoC 530 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 530 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, WiFi, etc.), and/or information services (e.g., navigation systems,rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 500. For example, the infotainment SoC 530 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, WiFi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 534, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 530 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 538,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 530 may include GPU functionality. The infotainmentSoC 530 may communicate over the bus 502 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 500. Insome examples, the infotainment SoC 530 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 536(e.g., the primary and/or backup computers of the vehicle 500) fail. Insuch an example, the infotainment SoC 530 may put the vehicle 500 into achauffeur to safe stop mode, as described herein.

The vehicle 500 may further include an instrument cluster 532 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 532 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 532 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 530 and theinstrument cluster 532. In other words, the instrument cluster 532 maybe included as part of the infotainment SoC 530, or vice versa.

FIG. 5D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 500 of FIG. 5A, inaccordance with some embodiments of the present disclosure. The system576 may include server(s) 578, network(s) 590, and vehicles, includingthe vehicle 500. The server(s) 578 may include a plurality of GPUs584(A)-584(H) (collectively referred to herein as GPUs 584), PCIeswitches 582(A)-582(H) (collectively referred to herein as PCIe switches582), and/or CPUs 580(A)-580(B) (collectively referred to herein as CPUs580). The GPUs 584, the CPUs 580, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 588 developed by NVIDIA and/orPCIe connections 586. In some examples, the GPUs 584 are connected viaNVLink and/or NVSwitch SoC and the GPUs 584 and the PCIe switches 582are connected via PCIe interconnects. Although eight GPUs 584, two CPUs580, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 578 mayinclude any number of GPUs 584, CPUs 580, and/or PCIe switches. Forexample, the server(s) 578 may each include eight, sixteen, thirty-two,and/or more GPUs 584.

The server(s) 578 may receive, over the network(s) 590 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 578 may transmit, over the network(s) 590 and to the vehicles,neural networks 592, updated neural networks 592, and/or map information594, including information regarding traffic and road conditions. Theupdates to the map information 594 may include updates for the HD map522, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 592, the updated neural networks 592, and/or the mapinformation 594 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 578 and/or other servers).

The server(s) 578 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Once the machinelearning models are trained, the machine learning models may be used bythe vehicles (e.g., transmitted to the vehicles over the network(s) 590,and/or the machine learning models may be used by the server(s) 578 toremotely monitor the vehicles.

In some examples, the server(s) 578 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 578 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 584, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 578 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 578 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 500. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 500, suchas a sequence of images and/or objects that the vehicle 500 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 500 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 500 is malfunctioning, the server(s) 578 may transmit asignal to the vehicle 500 instructing a fail-safe computer of thevehicle 500 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 578 may include the GPU(s) 584 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT 3).The combination of GPU-powered servers and inference acceleration maymake real-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 6 is a block diagram of an example computing device 600 suitablefor use in implementing some embodiments of the present disclosure.Computing device 600 may include a bus 602 that directly or indirectlycouples the following devices: memory 604, one or more centralprocessing units (CPUs) 606, one or more graphics processing units(GPUs) 608, a communication interface 610, input/output (I/O) ports 612,input/output components 614, a power supply 616, and one or morepresentation components 618 (e.g., display(s)).

Although the various blocks of FIG. 6 are shown as connected via the bus602 with lines, this is not intended to be limiting and is for clarityonly. For example, in some embodiments, a presentation component 618,such as a display device, may be considered an I/O component 614 (e.g.,if the display is a touch screen). As another example, the CPUs 606and/or GPUs 608 may include memory (e.g., the memory 604 may berepresentative of a storage device in addition to the memory of the GPUs608, the CPUs 606, and/or other components). In other words, thecomputing device of FIG. 6 is merely illustrative. Distinction is notmade between such categories as “workstation,” “server,” “laptop,”“desktop,” “tablet,” “client device,” “mobile device,” “hand-helddevice,” “game console,” “electronic control unit (ECU),” “virtualreality system,” and/or other device or system types, as all arecontemplated within the scope of the computing device of FIG. 6.

The bus 602 may represent one or more busses, such as an address bus, adata bus, a control bus, or a combination thereof. The bus 602 mayinclude one or more bus types, such as an industry standard architecture(ISA) bus, an extended industry standard architecture (EISA) bus, avideo electronics standards association (VESA) bus, a peripheralcomponent interconnect (PCI) bus, a peripheral component interconnectexpress (PCIe) bus, and/or another type of bus.

The memory 604 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 600. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 604 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device600. As used herein, computer storage media does not comprise signalsper se.

The communication media may embody computer-readable instructions, datastructures, program modules, and/or other data types in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” mayrefer to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, the communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media.

The CPU(s) 606 may be configured to execute the computer-readableinstructions to control one or more components of the computing device600 to perform one or more of the methods and/or processes describedherein. The CPU(s) 606 may each include one or more cores (e.g., one,two, four, eight, twenty-eight, seventy-two, etc.) that are capable ofhandling a multitude of software threads simultaneously. The CPU(s) 606may include any type of processor, and may include different types ofprocessors depending on the type of computing device 600 implemented(e.g., processors with fewer cores for mobile devices and processorswith more cores for servers). For example, depending on the type ofcomputing device 600, the processor may be an ARM processor implementedusing Reduced Instruction Set Computing (RISC) or an x86 processorimplemented using Complex Instruction Set Computing (CISC). Thecomputing device 600 may include one or more CPUs 606 in addition to oneor more microprocessors or supplementary co-processors, such as mathco-processors.

The GPU(s) 608 may be used by the computing device 600 to rendergraphics (e.g., 3D graphics). The GPU(s) 608 may include hundreds orthousands of cores that are capable of handling hundreds or thousands ofsoftware threads simultaneously. The GPU(s) 608 may generate pixel datafor output images in response to rendering commands (e.g., renderingcommands from the CPU(s) 606 received via a host interface). The GPU(s)608 may include graphics memory, such as display memory, for storingpixel data. The display memory may be included as part of the memory604. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link) When combined together, each GPU 608 may generatepixel data for different portions of an output image or for differentoutput images (e.g., a first GPU for a first image and a second GPU fora second image). Each GPU may include its own memory, or may sharememory with other GPUs.

In examples where the computing device 600 does not include the GPU(s)608, the CPU(s) 606 may be used to render graphics.

The communication interface 610 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 610 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The I/O ports 612 may enable the computing device 600 to be logicallycoupled to other devices including the I/O components 614, thepresentation component(s) 618, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 600.Illustrative I/O components 614 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 614 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 600. Thecomputing device 600 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 600 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 600 to render immersive augmented reality or virtual reality.

The power supply 616 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 616 may providepower to the computing device 600 to enable the components of thecomputing device 600 to operate.

The presentation component(s) 618 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 618 may receivedata from other components (e.g., the GPU(s) 608, the CPU(s) 606, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: receiving first image datarepresentative of a first image generated by a first sensor with a firstfield of view during a unit of time and second image data representativeof a second image generated by a second sensor with a second field ofview during the same unit of time; applying the first image data and thesecond image data to a neural network; computing, by the neural network,a first disparity map corresponding to the first image and a seconddisparity map corresponding to the second image; computing a first lossbased at least in part on comparing the first image and the second imageusing at least one of the first disparity map or the second disparitymap; receiving LIDAR data generated by at least one LIDAR sensor duringthe same unit of time; computing a second loss based at least in part oncomparing the LIDAR data to at least one of the first disparity map orthe second disparity map; and updating one or more parameters of theneural network based at least in part on the first loss and the secondloss.
 2. The method of claim 1, wherein the first sensor and the secondsensor include one or more stereo cameras.
 3. The method of claim 1,wherein the comparing the LIDAR data to at least one of the firstdisparity map or the second disparity map includes: determiningpredicted depth values corresponding to the at least one of the firstdisparity map or the second disparity map; and comparing the predicteddepth values to ground truth depth values corresponding to the LIDARdata.
 4. The method of claim 1, wherein: the neural network includes afirst stream of layers corresponding to the first image and a secondstream of layers corresponding to the second image; and first weightsfrom one or more layers of the first stream of layers and second weightsfrom one or more layers of the second stream of layers are shared duringtraining of the neural network.
 5. The method of claim 1, wherein thecomparing the first image and the second image using at least one of thefirst disparity map or the second disparity map includes: transformingat least one of the first image using the first disparity map togenerate a transformed first image or the second image using the seconddisparity map to generate a transformed second image; and comparing atleast one of the first transformed image to the second image using asimilarity metric or the second transformed image to the first imageusing the similarity metric.
 6. A method comprising: determining a firstcost volume and a second cost volume based at least in part on one ormore comparisons between first data representative of a first image of afirst field of view of a first sensor and second data representative ofa second image of a second field of view of a second sensor; applyingthe first cost volume and the second cost volume to matching layers of amachine learning model; computing, by the matching layers, matchingcosts between first pixels corresponding to the first image and secondpixels corresponding to the second image; applying the matching costs toa machine learned (ML) argmax function of the machine learning model,the ML argmax function including at least one convolutional layer; andcomputing, using at least the ML argmax function, first disparity valuescorresponding to each of the first pixels and second disparity valuescorresponding to each of the second pixels.
 7. The method of claim 6,further comprising: receiving, at one or more feature extraction layersof the machine learning model, the first data and the second data; andgenerating, by the one or more feature extraction layers, a firstfeature map corresponding to the first image and a second feature mapcorresponding to the second image; wherein the one or more comparisonsare between the first feature map and the second feature map.
 8. Themethod of claim 6, wherein the matching layers include one or moreconvolutional layers followed by one or more deconvolutional layers. 9.The method of claim 6, wherein the ML argmax function is differentiable,and the at least one convolutional layer includes parameters trainedusing back-propagation.
 10. The method of claim 6, wherein the machinelearning model includes one or more exponential linear unit (ELU)activation functions.
 11. The method of claim 6, wherein the machinelearning model does not include at least one of a batch normalizationlayer or a rectified linear unit (ReLU) activation function.
 12. Themethod of claim 6, wherein the computing the first disparity values andthe second disparity values is further based at least in part on using asigmoid activation function on an output of the ML argmax function. 13.The method of claim 6, wherein the method is executed on one or moreembedded graphics processing units (GPUs).
 14. The method of claim 6,further comprising: converting the matching costs to probability values,wherein the applying the matching costs to the ML argmax functionincludes applying the probability values to the ML argmax function. 15.The method of claim 6, wherein the machine learning model is a neuralnetwork, and the neural network is trained using photometric consistencyin an unsupervised training mode or using a combination of photometricconsistency and LIDAR data in a supervised training mode.
 16. A neuralnetwork comprising: feature extractor layers that receive first datarepresentative of a first image of a first field of view of a firstsensor and second data representative of a second image of a secondfield of view of a second sensor and compute a first feature mapcorresponding to the first image and a second feature map correspondingto the second image; cost volume layers representative of a first costvolume and a second cost volume computed based at least in part on oneof concatenating or correlating the first feature map with the secondfeature map; matching layers that compute, based at least in part on thefirst cost volume and the second cost volume, matching costs betweenfirst pixels corresponding to the first image and second pixelscorresponding to the second image; one or more machine learned (ML)argmax layers that execute an argmax function to compute, based at leastin part on the matching costs, first initial disparity valuescorresponding to each of the first pixels and second initial disparityvalues corresponding to each of the second pixels; and one or moresigmoid layers that execute a sigmoid function to convert the firstinitial disparity values to first final disparity values and the secondinitial disparity values to second final disparity values.
 17. Theneural network of claim 16, wherein the matching layers include at leastone convolutional layer followed by at least one deconvolutional layer.18. The neural network of claim 16, wherein the at least one ML argmaxlayer includes a plurality of convolutional layers.
 19. The neuralnetwork of claim 16, wherein the matching layers include at least onethree-dimensional convolutional layer.
 20. The neural network of claim16, further comprising at least one exponential linear unit (ELU) layerand no batch normalization layers.